Adventures is a new offering, which aligns with Airbnb’s mission to make travelers feel like they “belong anywhere.” So the goal of this feature is to be to diversify Airbnb’s revenue stream with a new offer that appeals to its base. Adventures are multi-day trips led by local experts-activities, meals, and stays included; in a nutshell, Adventures is a mix of Airbnb’s base offer for a place to stay and Airbnb Experiences feature.
The success of Adventure feature depends upon how it appeals to the two main parties: Hosts/supply and Travelers/demand. In order to stay organized, we will divide the metrics for each party based on the demand-supply equilibrium; in this post, we will discuss the demand side for travelers.
Demand-Side: Travelers
On the demand side, the basic metric is number of Adventures bookings; however, in order to drive better decisions and optimize the feature we need to map out the customer journey to figure how well this feature drives conversion. A journey map helps us identify which behaviours indicate success or failure towards conversion and define metrics to measure them numerically. We will then prioritize metrics based on two attributes: how core they are to evaluating success and how actionable they are. As shown in table below, customer journey has been mapped throughout the funnel:
In this journey user goes through a typical awareness, engagement, and conversion journey. There are also two post-experience stages: retention and referral. At each stage, use takes actions to get down the funnel and increasing the goal of increasing revenue.
For metrics, there are two high level success measures:
- Rate of bookings per month
- Revenue per month for bookings
Keep in mind that above key metrics are not actionable and lagging KPIs (aka. they show results at one point in time).
In order to standardize our metrics, we need to know the cycle of each metric effect; we will pick a monthly denominator to tracks the metrics in this case.
As shown in the table, there are 17 metrics listed; now, there is no way you’d be able to track all, nor it makes business sense. We will now need to prioritize based on importance and actionable ones. Using a typical quadrant will help us visualize this distinction:
As shown in above graph, we want to keep our focus on quadrants I & II as they have the highest importance to our main goal of driving revenue from Adventures booking feature and are actionable as much as possible. Quadrant III metrics aren’t important to core metric but are a gold mine for finding ways to optimize the feature. Below is a detailed explanation for each metric:
Adventure Tab Click Rate (#1) belongs to quadrant I as it is core metric to track revenue from the feature; it is also actionable since it tells we need to improve UX of the tab itself.
% of people clicked on an Adventure (#2) goes into quadrant II and is also a core metric because it is a funnel metric where user stage changes from Awareness to Engagement. However, it is not actionable because it does not provide insights into how to improve the product.
# clicks per activity (#3) does not help with bookings or revenue for the feature not it does give any actionable insight on how to improve; therefor it goes under quadrant III.
% drop-offs from adventures page (#4) is also a core metric because it is a funnel metric. It is not actionable because it does not provide insights into how to improve the product. Therefore, this metric falls under quadrant II.
Adventure price vs. # of drop-offs (#5) does not directly help measure or predict the number of bookings, so it is not core. However, it gives us direct insight that price might be the reason for drop-off; therefore, it belongs to quadrant III.
Number of adventures vs. # of drop-offs (#6) does not directly help measure or predict the number of bookings, so it is not core. This metric is highly actionable though because it points to the need to list more activities, so it falls under quadrant III.
# of activities which were full vs. # of drop-offs (#7) does not directly help measure or predict the number of bookings, so it is not a core metric either. However, like #5 and #6, it is highly actionable because it reveals that there is a need for making sure that users have multiple alternatives for activities. So, this metric falls under quadrant III.
Booking rate per month (#8) directly helps measure bookings and revenue, so it is a core metric. It does not help with insights into how to improve the product; therefore, this metric falls under quadrant II.
#Bookings per activity (#9) doesn’t help measure with core metrics not it gives clear direction on what to improve, therefore it belongs to quadrant IV.
$Bookings per month (#10) directly helps measure bookings and revenue, so it is a core metric. It however does not help with actionable insights into how to improve the product; therefore this metric falls under quadrant II.
% drop-offs from a single page (#11) does not directly help measure or predict the number of bookings, so it is not core. And, it is not actionable because it does not provide insights into how to improve the product. So, it falls under quadrant IV.
% Bookings from top of funnel (#12) is a core metric because it is a funnel metric. It does not provide insights into how to improve the product, so it is not actionable. So, this metric falls under quadrant II.
% Bookings from top of engagement stage (#13) is not a core metric as it doesn’t drive the revenue not it provides actionable insight; therefore it goes under quadrant IV.
Cancellations rate (#14) helps in measuring potential revenue lost and it is directly related to revenue; we will take it as core metric. But, it is not actionable, because it does not provide insights into what is wrong. So, it falls under quadrant II.
#Adventures booked Vs. Rooms Booked Vs. Experience Booked (#15) is a correlation graph between the 3 metrics; it doesn’t help with core metrics, however it gives us insights on the effect each of the offerings have on Adventure revenue; therefore, it is a quadrant III metric.
#referrals per user or Virality rate (#16) is a core metric because it can help predict revenue however it doesn’t give insights on to improve the feature; therefore, it goes under quadrant II.
#referrals per activity (#17) is not a core metric because it does not tie directly to bookings or revenue. And, it does not provide insights into how to improve the feature. So, it falls under quadrant IV.